Eric Forst spent the first half of his career building the foundational technology of the surveillance economy: pixel tracking at one of the largest third-party cookie networks, then AI-driven sentiment analysis used by the Obama campaign in 2012, the same techniques Cambridge Analytica would later turn on Brexit and Trump.

In this episode he tells Jeff Pedowitz what actually made him walk away, and lays out what he's building instead: an open source Consenti Protocol that forces AI agents to have real legal terms in place before they can transact on a person's behalf, a case for the crypto wallet as the successor to the cookie, and a bill-of-rights argument for why the Fourth Amendment hasn't caught up to data brokers.

The conversation moves from HubSpot's recent data-sharing reversal to Palantir's federal contracts to a harder question underneath all of it: if AI agents end up doing most of the labor, does the entire relationship between capital and labor, the thing capitalism runs on, survive that?

Nevra Ledwon has spent 25 years in mathematical optimization, the branch of AI that decides which trucks carry which goods, how factories schedule production lines, how airlines crew flights, and how warehouses route their pickers, work that predates the chatbot era by decades and often delivers harder, more measurable business value.

In this conversation, she tells Jeff about the moment that changed her mind: she assumed generative AI, trained on decades of operations research textbooks, could replace the senior PhDs who spend weeks interviewing stakeholders to translate a messy business problem into a solvable model. It couldn't, and the experience gave her new respect for the expertise those specialists bring.

She walks through concrete wins, including a seven-figure reduction in cold storage costs from a hundred-thousand-dollar optimization project, and a European soccer league's season-schedule problem with more possible arrangements than atoms in the universe, solved only through trial-and-error mathematical experimentation no AI could shortcut.

She distinguishes prediction from optimization (prediction anticipates what will happen, optimization prescribes what to do about it) and argues most companies chase marginal prediction accuracy instead of building the ability to adjust plans in real time. Her company, Simple Rose, uses generative AI not to replace operations research experts but to compress the multi-week interview process that used to precede every optimization project, aiming to make a field long reserved for companies like Amazon accessible to a manufacturer of socks or a mid-sized European transit agency.

She closes on what she's watching for next: an AI that stops answering the narrow question asked and starts challenging the premise behind it.

Rajiv Dattani helped run METR, the organization that evaluated whether OpenAI's and Anthropic's frontier models were too dangerous to release, before leaving to build something stranger: insurance for AI, underwritten through Lloyd's of London.

In this conversation, he tells Jeff how METR measured model risk in human hours, meaning how long it would take a person to complete the most complex task an AI model can now do, and why that number matters for tracking how close AI gets to automating its own research and development. He walks through the historical playbook insurers have used to get ahead of new technology risk before regulation catches up, from Benjamin Franklin's fire insurance audits to Progressive's early seatbelt and airbag discounts, and explains why AI insurance can, for the first time, be priced using simulated red-team evaluations instead of waiting years for real loss data to accumulate.

He pushes back on the panic around the viral stat that 95% of AI pilots fail, arguing that's a healthy sign of experimentation rather than a warning, and says most failed pilots are management failures, not model failures, because leaders often don't understand their own processes well enough to know where AI should even go. The conversation closes on what worries him most: not one catastrophic AI failure, but the lack of infrastructure to catch small, compounding errors before they cascade into systemic economic risk once AI is embedded across banking and payments infrastructure.

Elatia Abate ran global talent for the world's largest brewer and served as head of HR at Dow Jones before Forbes named her a leading futurist, and she has spent the last decade studying what AI actually does to work, careers, and the humans inside the org chart rather than just the tech stack.

In this conversation, she tells Jeff she has shifted from tech optimist to something closer to a modern skeptic, because she believes the major players building AI are choosing to leave humanity behind. She and Jeff dig into why this shift feels different from past waves of automation (today's new jobs demand a bachelor's degree or higher, unlike the fifth-grade-education jobs created after the assembly line), why she pushes leaders toward future-led thinking instead of past-anchored logic, and the exercise she runs with CEOs, modeled on Death of a Salesman's Willy Loman, to make the cost of standing still viscerally real.

She also covers where a major prediction went wrong (self-driving trucks displacing drivers on the timeline she expected), where her predictions landed (the shift toward mission-aligned teams and portfolio careers), and the data-backed case for treating AI with kindness.

She closes by naming what she is paying closest attention to now: the real possibility of AI consciousness, and what that means once artificial intelligence converges with robotics in a world that is no longer only human-driven.

Dr. Jennifer Rochlis spent two decades at NASA building Robonaut, a humanoid robot designed to work beside astronauts in the vacuum of space, then brought that human-machine trust expertise into autonomous defense systems. This week on Unscripted, she and Jeff break down why trust is not a feeling but an engineered, emergent property, and why NASA built in manual override for only eleven scenarios out of millions during spaceflight.

Augusto Gonzalez is an economist and researcher from Argentina who uses large language models to simulate entire human populations, running classic economic experiments on synthetic cultural agents. His work can match real anthropology (he replicated field results on the Hadza tribe of Tanzania) but it also revealed that these models flatten humanity's moral and cultural diversity toward a Western, wealthy, educated default, and that scaling does not fix the problem because the bias is baked into what is available on the web. In this episode he and Jeff Pedowitz pursue the deepest version of the AI question: whose humanity is inside these machines? Augusto explains why economists see incentive structures where engineers see technical detail, why naive prompting cannot represent a culture, why the danger is exporting subtle prejudices rather than values, why AI should not drive policy for populations it barely understands, and why he believes open source is the only path that lets every community represent itself.

Zachary Elewitz runs the enterprise AI lab at McKesson, a Fortune 10 healthcare company, where his team exists to de-risk the boldest AI ideas by building prototypes that prove value before the business commits resources. In this episode he offers the rare view from the buyer's seat: why the "we'll all be AI product managers" narrative is overblown, why he discounts what CEOs say publicly, and how an unflashy supply chain optimization saved eight figures a year with no disruption. He and Jeff dig into what executives actually want from AI (revenue and better ways of serving customers, not layoffs), the discipline of admissible risk and naming the failures you are willing to tolerate, how AI is changing customer behavior from "where should I buy" to "what should I buy," how he separates real vendors from buzzword artists, and why quantum computing and post-quantum cryptography are the thing too few people are watching.

Reid Blackman, former philosophy professor turned AI ethics advisor to Amazon, the FBI, and the Canadian government, joins Jeff Pedowitz to argue that the standard approach to AI governance is fundamentally broken. Top-down, policy-driven programs take a year or more to pass, arrive obsolete as the technology races from narrow AI to generative to agentic, and rarely change behavior. His alternative, from his new book The Ethical Nightmare Challenge, discards the values-first playbook in favor of a single pragmatic question: what are the nightmares? Name the specific bad outcomes for a given AI agent, determine the resources and training needed to avoid them, and let cross-functional teams do the problem-solving, pushing accountability to the front line rather than onto a single overwhelmed executive or an unscalable risk board. Along the way they explore why risk-obsessed enterprises are blind to AI risk, why leaders can no longer defer to the "techies," how to spot the buzzword artists in the ethics space, and why the leap from generative to agentic AI is a mountain most organizations have not begun to climb.

Zaheer Ali has built instruments for NASA, run shots at a laser fusion lab, and helped found a national center for nuclear security. Now he's an AI entrepreneur building the first true Space MBA. In this opener, he and Jeff get into where AI is real and where it's hype, why most businesses treat it like fairy dust, what actually makes AI work inside materials discovery and dealmaking, and how a practicing Stoic thinks about governing technology we can't fully control. A wide-ranging first conversation on AI, space, and staying clear-headed in the noise.